SlideShare uma empresa Scribd logo
1 de 14
Customer Support
in the
Big Data Era
TANYA SHASTRI
@tanyashas3
What will be discussed
 Customer support context
 From data to business benefit
 Methodology
 Metrics
 Sample analysis
 Preparing the data
 Considerations and learnings
Customer Support Context
Customer Support Data Sources
 Incident databases, call center data
 Customer self-help website
 In-product or in-app data, call home data
 Discussion Forums
Disparate ● Disjoint ● Silo-ed
Structured ● Semi-structured ● Unstructured
Transactions ● Events ● Logs
Volume ● Velocity ● Variety
Methodology and Metrics
 Tracking and improving self-help
 Troubleshooting page score
 In-product proactive support
 Customer behavior based learning
 Improving support efficiency
Tracking and improving self-help
 Crude score
ScoreC = #incidentsP1
#“P1 help page hits”
 Detailed score
ScoreD = (#incidentsP1 - #rmaP1)
#“sessions P1 help page hits with t>10s”
P1  Product1
RMA  Return Merchandise Authorization
t  duration of time spent on the page
Self-help score (crude)
0
500
1,000
1,500
2,000
2,500
Product 1 Product 2
ScoreC
HITS/1000 INCIDENTS SCOREx1000
Self-help score (detailed)
0
500
1,000
1,500
Product 1 Product 2
ScoreD
S-HITS/1000 INCIDENTS-RMA SCOREx1000
In-product proactive support
 Support integral part of product development
 Customer behavior driven analysis
 Analysis based on a sequence of actions
ACTION 1
#A1 reach here
T2 spent here
ACTION 2
#A2 reach here
T2 spent here
ACTION 3
#A3 reach here
T3 spent here
Analysis of sequence of actions
0
20
40
60
80
100
120
140
0
10000
20000
30000
40000
50000
60000
Action 1 Action 2 Action 3 Action 4
#taking this action Avg Time (sec)
Discussion Forums and Reviews
 Discussion Forums and Reviews
 Term frequency trend
 Sentiment analysis
 Time at each step in actual support process
Preparing the Data
 Standardizing Product nomenclature
 Enriching events/logs with traditional data
 Session based analysis, session segmentation
 Product hierarchy for drill-down to various levels
Be aware that…
 The process is likely to be iterative
 Data prep is a big deal
 With “big data” simple analysis can be valuable
 Analysis can sometimes feel like “hindsight is 20/20”
Questions?

Mais conteúdo relacionado

Semelhante a Customer Support in the Big Data Era

La Dove Associates -- CRM/Customer Care Consulting Overview
La Dove Associates --  CRM/Customer Care Consulting Overview La Dove Associates --  CRM/Customer Care Consulting Overview
La Dove Associates -- CRM/Customer Care Consulting Overview LaDove Associates
 
Lessons Learned - Insights to Improve Support for MS Teams in a Hybrid Work E...
Lessons Learned - Insights to Improve Support for MS Teams in a Hybrid Work E...Lessons Learned - Insights to Improve Support for MS Teams in a Hybrid Work E...
Lessons Learned - Insights to Improve Support for MS Teams in a Hybrid Work E...panagenda
 
Accelerate ROI for Microsoft 365 through Improved Digital Experience Monitoring
Accelerate ROI for Microsoft 365 through Improved Digital Experience MonitoringAccelerate ROI for Microsoft 365 through Improved Digital Experience Monitoring
Accelerate ROI for Microsoft 365 through Improved Digital Experience Monitoringpanagenda
 
Retail Design
Retail DesignRetail Design
Retail Designjagishar
 
Datawarehouse Overview
Datawarehouse OverviewDatawarehouse Overview
Datawarehouse Overviewashok kumar
 
Growing your business with BPM
Growing your business with BPMGrowing your business with BPM
Growing your business with BPMinnovelocity
 
Warehouse components
Warehouse componentsWarehouse components
Warehouse componentsganblues
 
Exploring the Data science Process
Exploring the Data science ProcessExploring the Data science Process
Exploring the Data science ProcessVishal Patel
 
Artificial Intelligence high ROI case studies from around the world: approach...
Artificial Intelligence high ROI case studies from around the world: approach...Artificial Intelligence high ROI case studies from around the world: approach...
Artificial Intelligence high ROI case studies from around the world: approach...Data Driven Innovation
 
Big Data & Analytics 101: How Customer Lifetime Value Enhances Predictive Mar...
Big Data & Analytics 101: How Customer Lifetime Value Enhances Predictive Mar...Big Data & Analytics 101: How Customer Lifetime Value Enhances Predictive Mar...
Big Data & Analytics 101: How Customer Lifetime Value Enhances Predictive Mar...Big Cloud Analytics, Inc.
 
Techmetrics Of Dat Project Code And Designs
Techmetrics Of Dat Project Code And DesignsTechmetrics Of Dat Project Code And Designs
Techmetrics Of Dat Project Code And DesignsErin Perez
 
Improving the Business Processes
Improving the Business ProcessesImproving the Business Processes
Improving the Business Processesfmbabs49000
 
Webinar: Transforming Customer Experience Through an Always-On Data Platform
Webinar: Transforming Customer Experience Through an Always-On Data PlatformWebinar: Transforming Customer Experience Through an Always-On Data Platform
Webinar: Transforming Customer Experience Through an Always-On Data PlatformDataStax
 
SaaS.City 2017 Customer Success Bootcamp
SaaS.City 2017 Customer Success BootcampSaaS.City 2017 Customer Success Bootcamp
SaaS.City 2017 Customer Success BootcampGainsight
 
SaaS.City Customer Success Bootcamp at SaaStock 2017
SaaS.City Customer Success Bootcamp at SaaStock 2017SaaS.City Customer Success Bootcamp at SaaStock 2017
SaaS.City Customer Success Bootcamp at SaaStock 2017SaaStock
 
AWS Webcast - Sales Productivity Solutions with MicroStrategy and Redshift
AWS Webcast - Sales Productivity Solutions with MicroStrategy and RedshiftAWS Webcast - Sales Productivity Solutions with MicroStrategy and Redshift
AWS Webcast - Sales Productivity Solutions with MicroStrategy and RedshiftAmazon Web Services
 
Operationalizing Customer Analytics with Azure and Power BI
Operationalizing Customer Analytics with Azure and Power BIOperationalizing Customer Analytics with Azure and Power BI
Operationalizing Customer Analytics with Azure and Power BICCG
 

Semelhante a Customer Support in the Big Data Era (20)

La Dove Associates -- CRM/Customer Care Consulting Overview
La Dove Associates --  CRM/Customer Care Consulting Overview La Dove Associates --  CRM/Customer Care Consulting Overview
La Dove Associates -- CRM/Customer Care Consulting Overview
 
Big Data and Customer Experience
Big Data and Customer ExperienceBig Data and Customer Experience
Big Data and Customer Experience
 
Lessons Learned - Insights to Improve Support for MS Teams in a Hybrid Work E...
Lessons Learned - Insights to Improve Support for MS Teams in a Hybrid Work E...Lessons Learned - Insights to Improve Support for MS Teams in a Hybrid Work E...
Lessons Learned - Insights to Improve Support for MS Teams in a Hybrid Work E...
 
Accelerate ROI for Microsoft 365 through Improved Digital Experience Monitoring
Accelerate ROI for Microsoft 365 through Improved Digital Experience MonitoringAccelerate ROI for Microsoft 365 through Improved Digital Experience Monitoring
Accelerate ROI for Microsoft 365 through Improved Digital Experience Monitoring
 
Retail Design
Retail DesignRetail Design
Retail Design
 
Datawarehouse Overview
Datawarehouse OverviewDatawarehouse Overview
Datawarehouse Overview
 
Growing your business with BPM
Growing your business with BPMGrowing your business with BPM
Growing your business with BPM
 
Warehouse components
Warehouse componentsWarehouse components
Warehouse components
 
Exploring the Data science Process
Exploring the Data science ProcessExploring the Data science Process
Exploring the Data science Process
 
Andy Malone - Microsoft office 365 security deep dive
Andy Malone - Microsoft office 365 security deep diveAndy Malone - Microsoft office 365 security deep dive
Andy Malone - Microsoft office 365 security deep dive
 
Artificial Intelligence high ROI case studies from around the world: approach...
Artificial Intelligence high ROI case studies from around the world: approach...Artificial Intelligence high ROI case studies from around the world: approach...
Artificial Intelligence high ROI case studies from around the world: approach...
 
Big Data & Analytics 101: How Customer Lifetime Value Enhances Predictive Mar...
Big Data & Analytics 101: How Customer Lifetime Value Enhances Predictive Mar...Big Data & Analytics 101: How Customer Lifetime Value Enhances Predictive Mar...
Big Data & Analytics 101: How Customer Lifetime Value Enhances Predictive Mar...
 
Techmetrics Of Dat Project Code And Designs
Techmetrics Of Dat Project Code And DesignsTechmetrics Of Dat Project Code And Designs
Techmetrics Of Dat Project Code And Designs
 
Improving the Business Processes
Improving the Business ProcessesImproving the Business Processes
Improving the Business Processes
 
Agile Data Science
Agile Data ScienceAgile Data Science
Agile Data Science
 
Webinar: Transforming Customer Experience Through an Always-On Data Platform
Webinar: Transforming Customer Experience Through an Always-On Data PlatformWebinar: Transforming Customer Experience Through an Always-On Data Platform
Webinar: Transforming Customer Experience Through an Always-On Data Platform
 
SaaS.City 2017 Customer Success Bootcamp
SaaS.City 2017 Customer Success BootcampSaaS.City 2017 Customer Success Bootcamp
SaaS.City 2017 Customer Success Bootcamp
 
SaaS.City Customer Success Bootcamp at SaaStock 2017
SaaS.City Customer Success Bootcamp at SaaStock 2017SaaS.City Customer Success Bootcamp at SaaStock 2017
SaaS.City Customer Success Bootcamp at SaaStock 2017
 
AWS Webcast - Sales Productivity Solutions with MicroStrategy and Redshift
AWS Webcast - Sales Productivity Solutions with MicroStrategy and RedshiftAWS Webcast - Sales Productivity Solutions with MicroStrategy and Redshift
AWS Webcast - Sales Productivity Solutions with MicroStrategy and Redshift
 
Operationalizing Customer Analytics with Azure and Power BI
Operationalizing Customer Analytics with Azure and Power BIOperationalizing Customer Analytics with Azure and Power BI
Operationalizing Customer Analytics with Azure and Power BI
 

Mais de DataWorks Summit

Floating on a RAFT: HBase Durability with Apache Ratis
Floating on a RAFT: HBase Durability with Apache RatisFloating on a RAFT: HBase Durability with Apache Ratis
Floating on a RAFT: HBase Durability with Apache RatisDataWorks Summit
 
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiTracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
 
HBase Tales From the Trenches - Short stories about most common HBase operati...
HBase Tales From the Trenches - Short stories about most common HBase operati...HBase Tales From the Trenches - Short stories about most common HBase operati...
HBase Tales From the Trenches - Short stories about most common HBase operati...DataWorks Summit
 
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
 
Managing the Dewey Decimal System
Managing the Dewey Decimal SystemManaging the Dewey Decimal System
Managing the Dewey Decimal SystemDataWorks Summit
 
Practical NoSQL: Accumulo's dirlist Example
Practical NoSQL: Accumulo's dirlist ExamplePractical NoSQL: Accumulo's dirlist Example
Practical NoSQL: Accumulo's dirlist ExampleDataWorks Summit
 
HBase Global Indexing to support large-scale data ingestion at Uber
HBase Global Indexing to support large-scale data ingestion at UberHBase Global Indexing to support large-scale data ingestion at Uber
HBase Global Indexing to support large-scale data ingestion at UberDataWorks Summit
 
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixScaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixDataWorks Summit
 
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiBuilding the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
 
Supporting Apache HBase : Troubleshooting and Supportability Improvements
Supporting Apache HBase : Troubleshooting and Supportability ImprovementsSupporting Apache HBase : Troubleshooting and Supportability Improvements
Supporting Apache HBase : Troubleshooting and Supportability ImprovementsDataWorks Summit
 
Security Framework for Multitenant Architecture
Security Framework for Multitenant ArchitectureSecurity Framework for Multitenant Architecture
Security Framework for Multitenant ArchitectureDataWorks Summit
 
Presto: Optimizing Performance of SQL-on-Anything Engine
Presto: Optimizing Performance of SQL-on-Anything EnginePresto: Optimizing Performance of SQL-on-Anything Engine
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
 
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
 
Extending Twitter's Data Platform to Google Cloud
Extending Twitter's Data Platform to Google CloudExtending Twitter's Data Platform to Google Cloud
Extending Twitter's Data Platform to Google CloudDataWorks Summit
 
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiEvent-Driven Messaging and Actions using Apache Flink and Apache NiFi
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiDataWorks Summit
 
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerSecuring Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerDataWorks Summit
 
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...DataWorks Summit
 
Computer Vision: Coming to a Store Near You
Computer Vision: Coming to a Store Near YouComputer Vision: Coming to a Store Near You
Computer Vision: Coming to a Store Near YouDataWorks Summit
 
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkBig Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkDataWorks Summit
 

Mais de DataWorks Summit (20)

Data Science Crash Course
Data Science Crash CourseData Science Crash Course
Data Science Crash Course
 
Floating on a RAFT: HBase Durability with Apache Ratis
Floating on a RAFT: HBase Durability with Apache RatisFloating on a RAFT: HBase Durability with Apache Ratis
Floating on a RAFT: HBase Durability with Apache Ratis
 
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiTracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
 
HBase Tales From the Trenches - Short stories about most common HBase operati...
HBase Tales From the Trenches - Short stories about most common HBase operati...HBase Tales From the Trenches - Short stories about most common HBase operati...
HBase Tales From the Trenches - Short stories about most common HBase operati...
 
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
 
Managing the Dewey Decimal System
Managing the Dewey Decimal SystemManaging the Dewey Decimal System
Managing the Dewey Decimal System
 
Practical NoSQL: Accumulo's dirlist Example
Practical NoSQL: Accumulo's dirlist ExamplePractical NoSQL: Accumulo's dirlist Example
Practical NoSQL: Accumulo's dirlist Example
 
HBase Global Indexing to support large-scale data ingestion at Uber
HBase Global Indexing to support large-scale data ingestion at UberHBase Global Indexing to support large-scale data ingestion at Uber
HBase Global Indexing to support large-scale data ingestion at Uber
 
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixScaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
 
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiBuilding the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
 
Supporting Apache HBase : Troubleshooting and Supportability Improvements
Supporting Apache HBase : Troubleshooting and Supportability ImprovementsSupporting Apache HBase : Troubleshooting and Supportability Improvements
Supporting Apache HBase : Troubleshooting and Supportability Improvements
 
Security Framework for Multitenant Architecture
Security Framework for Multitenant ArchitectureSecurity Framework for Multitenant Architecture
Security Framework for Multitenant Architecture
 
Presto: Optimizing Performance of SQL-on-Anything Engine
Presto: Optimizing Performance of SQL-on-Anything EnginePresto: Optimizing Performance of SQL-on-Anything Engine
Presto: Optimizing Performance of SQL-on-Anything Engine
 
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
 
Extending Twitter's Data Platform to Google Cloud
Extending Twitter's Data Platform to Google CloudExtending Twitter's Data Platform to Google Cloud
Extending Twitter's Data Platform to Google Cloud
 
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiEvent-Driven Messaging and Actions using Apache Flink and Apache NiFi
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
 
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerSecuring Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
 
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
 
Computer Vision: Coming to a Store Near You
Computer Vision: Coming to a Store Near YouComputer Vision: Coming to a Store Near You
Computer Vision: Coming to a Store Near You
 
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkBig Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
 

Último

Comparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioComparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioChristian Posta
 
Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Commit University
 
Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024SkyPlanner
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...Aggregage
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemAsko Soukka
 
Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.YounusS2
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Will Schroeder
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IES VE
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesDavid Newbury
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopBachir Benyammi
 
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfJamie (Taka) Wang
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UbiTrack UK
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarPrecisely
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6DianaGray10
 
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDEADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDELiveplex
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxUdaiappa Ramachandran
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxMatsuo Lab
 
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationUsing IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationIES VE
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfAijun Zhang
 

Último (20)

Comparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioComparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and Istio
 
Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)
 
Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystem
 
Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond Ontologies
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 Workshop
 
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity Webinar
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6
 
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDEADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptx
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptx
 
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationUsing IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdf
 
20230104 - machine vision
20230104 - machine vision20230104 - machine vision
20230104 - machine vision
 

Customer Support in the Big Data Era

  • 1. Customer Support in the Big Data Era TANYA SHASTRI @tanyashas3
  • 2. What will be discussed  Customer support context  From data to business benefit  Methodology  Metrics  Sample analysis  Preparing the data  Considerations and learnings
  • 4. Customer Support Data Sources  Incident databases, call center data  Customer self-help website  In-product or in-app data, call home data  Discussion Forums Disparate ● Disjoint ● Silo-ed Structured ● Semi-structured ● Unstructured Transactions ● Events ● Logs Volume ● Velocity ● Variety
  • 5. Methodology and Metrics  Tracking and improving self-help  Troubleshooting page score  In-product proactive support  Customer behavior based learning  Improving support efficiency
  • 6. Tracking and improving self-help  Crude score ScoreC = #incidentsP1 #“P1 help page hits”  Detailed score ScoreD = (#incidentsP1 - #rmaP1) #“sessions P1 help page hits with t>10s” P1  Product1 RMA  Return Merchandise Authorization t  duration of time spent on the page
  • 7. Self-help score (crude) 0 500 1,000 1,500 2,000 2,500 Product 1 Product 2 ScoreC HITS/1000 INCIDENTS SCOREx1000
  • 8. Self-help score (detailed) 0 500 1,000 1,500 Product 1 Product 2 ScoreD S-HITS/1000 INCIDENTS-RMA SCOREx1000
  • 9. In-product proactive support  Support integral part of product development  Customer behavior driven analysis  Analysis based on a sequence of actions ACTION 1 #A1 reach here T2 spent here ACTION 2 #A2 reach here T2 spent here ACTION 3 #A3 reach here T3 spent here
  • 10. Analysis of sequence of actions 0 20 40 60 80 100 120 140 0 10000 20000 30000 40000 50000 60000 Action 1 Action 2 Action 3 Action 4 #taking this action Avg Time (sec)
  • 11. Discussion Forums and Reviews  Discussion Forums and Reviews  Term frequency trend  Sentiment analysis  Time at each step in actual support process
  • 12. Preparing the Data  Standardizing Product nomenclature  Enriching events/logs with traditional data  Session based analysis, session segmentation  Product hierarchy for drill-down to various levels
  • 13. Be aware that…  The process is likely to be iterative  Data prep is a big deal  With “big data” simple analysis can be valuable  Analysis can sometimes feel like “hindsight is 20/20”

Notas do Editor

  1. Hi I’m Tanya Shastri. A little big of background on myself before we get into the presentation. I’ve been plugged into the big data world for a while. A relatively short 5 years ago, there were no conferences, let alone conferences of this scale. Unconferences back in the day. Though not surprised at all to see the size of the conferences today. There is a lot of promise in data and tools like Hadoop are helping deliver on that promise. And while I’m not at Natero anymore, they are… I’ll be talking about how having access to big data can provide some actionable insights to reduce the cost of customer support and improve customer experience and customer satisfaction.
  2. Here’s how I’ve structured the presentation. First some context Then I’ll go into the methodology – and some sample analytics For the previous part we’ll assume that all the data has is automagically prepared.. So a spotlight on data prep because any of you who’ve worked with data will know that data prep is often if not always the harder part. We’ll end with some considerations and learnings…
  3. To provide a little bit of context for what kind of company this would apply to. A company that provides consumer products whether hardware or software. The can be extended to the internet of things in general.
  4. Disparate sources, geographically distributed, last thing you want to do is add another source.. A source that is “big”. Combining a couple sources In some cases even using a source of data that isn’t typically used
  5. Thousands of products – each having its own troubleshooting page. The intention of the troubleshooting page was to enable customers to self-help. There was no insight into how these troubleshooting pages were performing. If they were able to know which pages were not performing well, they could improve them and reduce incidents filed. For this two sources were used – the web-clickstream data from the support website and the incident database.
  6. Thousands of products – each having its own troubleshooting page. The intention of the troubleshooting page was to enable customers to self-help. There was no insight into how these troubleshooting pages were performing.
  7. Enterprises are looking for ways to improve customer satisfaction and reduce support costs, but often do not have actionable insights. Traditional approaches and tools fall short, often based on small biased datasets and requiring long turnaround times. This talk will cover the steps involved todevelop a big data solution for support through the example of a leading vendor of electronic consumer peripherals. Topics will include: The methodology and metrics developed:Metrics to track and improve self-help through support sites Metrics to track the end-to-end support process to find delays in processing of incidents, escalations, etc. Methods to identify problem areas based on data from discussion forums Data-driven discovery of paths that customers prefer for support The data wrangling required to implement the solution using a big data analytics platform:Big data analytics techniques to track customer behavior across channels Preparing the data for analytics: joining, merging and enriching the diverse datasets Validating the parameters and techniques used for analysis Considerations for an iterative analytical approach to get results with the highest confidence interval Metrics at various granularities to meet the needs of various business decision makers Automation for maintaining and tracking up-to-date results